Application of multi-source information fusion and graph neural networks in fault localization for complex agricultural machinery systems
TL;DR
This research introduces Multi-Source Graph-Enhanced Fault Localization (MS-GEFL) framework, a dynamic graph model is used to represent the mechanical system and Graph Neural Networks are used to propagate the information related to the fault across different operating conditions to identify the anomalies.
Abstract
The modern agricultural equipment needs to be able to cope with changes in field conditions, however, fault detection is still challenging for the time being with the traditional rule based or single sensor based diagnostic procedures. These methods frequently do not reflect data interactions and interdependencies within components. To overcome these drawbacks, this research introduces Multi-Source Graph-Enhanced Fault Localization (MS-GEFL) framework. The framework combines vibration sensors, operational logs, environmental data and controller outputs in a multi-source fusion layer to guarantee that the data is consistent. Then, a dynamic graph model is used to represent the mechanical system and Graph Neural Networks (GNNs) are used to propagate the information related to the fault across different operating conditions to identify the anomalies. The experimental results show that the precision, recall and F1 of MS-GEFL are 93.9%, 94.6% and 94.0% respectively. Additionally, the framework makes operations more efficient, with an average localization delay of 1.28 seconds and an average robustness index of 91.7% even in the presence of noise in sensor data. This technology is intelligent and reliable solution for fault localization in complex agricultural machinery.